Cloud services had been very popular in the recent decade. Cloud services are based on cloud computing to provide associated services or commodities without increasing burden on client side. Cloud computing involves a large number of computers connected through a communication network such as the Internet. It relies on sharing of resources to achieve coherence and economies of scale. At the foundation of cloud computing is the broader concept of converged infrastructure and shared services. Among all the shared services, memory and storage are definitely the two having maximum demand. This is because some hot applications, such as video streaming, require huge quantity of data to be stored. Management of memories and storages while the cloud services operate is very important to maintain normal service quality for the clients.
For example, a server used for providing cloud services usually manages or links to a number of Hard Disk Drives (HDDs). Clients access the server and data are read from or written to the HDDs. There are some problems, e.g. latency of response, due to limitation of the HDD system. Under normal operation of HDD system, the latency is usually caused by requirements of applications (i.e. workload), as the required access speed is higher than that the HDD system can support. In the past, latency might not be a problem since service providers of cloud service can offer infrastructure for the maximum capacity that can be expected. However, with more and more clients joining and sharing resources of the services, the fixed infrastructure may not be able to support the increasing requirement from the clients. To improve and strengthen the infrastructure, a flexible adjustment of the infrastructure is needed, in addition to only preparing more hardware. It is not economical to put all resources just for an unsure situation that might happen at a point of time in the future.
Another increasing demand as well as the cloud service is software defined storage. Software defined storage refers to computer data storage technologies which separate storage hardware from the software that manages the storage infrastructure. The software enabling a software defined storage environment provides policy management for feature options, such as deduplication, replication, thin provisioning, snapshots, and backup. With software defined storage technologies, the requirement of flexible adjustment of infrastructure can be fulfilled. Sufficient resources of the infrastructure can be used on time while unused hardware in the infrastructure can be standbys in order to save power consumption and prolong hardware life cycle. Besides the flexible adjustment of infrastructure, if the system can predict the storage traffic in the near future and adjust itself accordingly to match the requirements, the system can better serve the clients. In other words, it is high demand for a system being capable of predicting workload in a particular point in time in the future, and that is called traffic modeling. The storage traffic modeling is especially the main interest in this invention.
With reference to US Patent Publication No. 20090138420 which discloses a useful invention for the demand mentioned above. A method is for modeling network traffic in which an artificial neural network architecture is utilized in order to intelligently and adaptively model the capacity of a network. Initially, the network traffic is decomposed into a number of categories, such as individual users, application usage, or common usage groups. Inputs to one artificial neural network are then defined such that a respective combination of inputs permits prediction of bandwidth capacity needs for that input condition. Outputs of that artificial neural network are representative of the network traffic associated with the respective inputs. For example, a number of bandwidth profiles associated with respective categories may be defined. An artificial neural network is then constructed and trained with those bandwidth profiles and then utilized to predict future bandwidth needs for the network.
Application of artificial neural network for prediction of bandwidth profiles is the key portion in the invention. Detailed processes for implementing the invention are disclosed. However, the invention is basically used for prediction of bandwidth profiles for flights according to the embodiments. Under this situation, the infrastructure for the network traffic is almost fixed with just different workloads, e.g. passengers use the infrastructure from different group. Without linking to any cloud service, its hardware of infrastructure may be adjusted according to different workloads. Meanwhile, prediction of requirements of applications for a cloud service is not easily done by learning data from different categories. Precisely, the prediction should be made based on different performance parameters from one source. For example, by learning Input/output Operations Per Second (IOPS) of a hard disc drive (HDD) system and latency in a period of time in the past time to predict IOPS in the future.
Therefore, a new method and system for predicting storage traffic and providing a traffic modeling for a storage defined storage system based on an artificial neural network is still desired.